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Machine Learning And Data Science In The Power Generation Industry

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Machine Learning and Data Science in the Power Generation Industry

Machine Learning and Data Science in the Power Generation Industry Book
Author : Patrick Bangert
Publisher : Elsevier
Release : 2021-01-25
ISBN : 0128226005
Language : En, Es, Fr & De

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Book Description :

Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop machine learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study–driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting. Provides best practices on how to design and set up ML projects in power systems, including all nontechnological aspects necessary to be successful Explores implementation pathways, explaining key ML algorithms and approaches as well as the choices that must be made, how to make them, what outcomes may be expected, and how the data must be prepared for them Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems Accompanied by numerous supporting real-world case studies, providing practical evidence of both best practices and potential pitfalls

Machine Learning and Data Science in the Oil and Gas Industry

Machine Learning and Data Science in the Oil and Gas Industry Book
Author : Patrick Bangert
Publisher : Gulf Professional Publishing
Release : 2021-03-04
ISBN : 0128209143
Language : En, Es, Fr & De

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Book Description :

Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful Gain practical understanding of machine learning used in oil and gas operations through contributed case studies Learn change management skills that will help gain confidence in pursuing the technology Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)

Artificial Intelligence Machine Learning and Data Science Technologies

Artificial Intelligence  Machine Learning  and Data Science Technologies Book
Author : Neeraj Mohan,Ruchi Singla,Priyanka Kaushal,Seifedine Kadry
Publisher : CRC Press
Release : 2021-10-12
ISBN : 1000460541
Language : En, Es, Fr & De

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Book Description :

This book provides a comprehensive, conceptual, and detailed overview of the wide range of applications of Artificial Intelligence, Machine Learning, and Data Science and how these technologies have an impact on various domains such as healthcare, business, industry, security, and how all countries around the world are feeling this impact. The book aims at low-cost solutions which could be implemented even in developing countries. It highlights the significant impact these technologies have on various industries and on us as humans. It provides a virtual picture of forthcoming better human life shadowed by the new technologies and their applications and discusses the impact Data Science has on business applications. The book will also include an overview of the different AI applications and their correlation between each other. The audience is graduate and postgraduate students, researchers, academicians, institutions, and professionals who are interested in exploring key technologies like Artificial Intelligence, Machine Learning, and Data Science.

Applying Data Science

Applying Data Science Book
Author : Arthur K. Kordon
Publisher : Springer Nature
Release : 2020-09-12
ISBN : 3030363759
Language : En, Es, Fr & De

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Book Description :

This book offers practical guidelines on creating value from the application of data science based on selected artificial intelligence methods. In Part I, the author introduces a problem-driven approach to implementing AI-based data science and offers practical explanations of key technologies: machine learning, deep learning, decision trees and random forests, evolutionary computation, swarm intelligence, and intelligent agents. In Part II, he describes the main steps in creating AI-based data science solutions for business problems, including problem knowledge acquisition, data preparation, data analysis, model development, and model deployment lifecycle. Finally, in Part III the author illustrates the power of AI-based data science with successful applications in manufacturing and business. He also shows how to introduce this technology in a business setting and guides the reader on how to build the appropriate infrastructure and develop the required skillsets. The book is ideal for data scientists who will implement the proposed methodology and techniques in their projects. It is also intended to help business leaders and entrepreneurs who want to create competitive advantage by using AI-based data science, as well as academics and students looking for an industrial view of this discipline.

Strategic Approaches to Energy Management

Strategic Approaches to Energy Management Book
Author : Serhat Yüksel
Publisher : Springer Nature
Release : 2021-10-26
ISBN : 3030767833
Language : En, Es, Fr & De

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Book Description :

Download Strategic Approaches to Energy Management book written by Serhat Yüksel, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

IoT Machine Learning Applications in Telecom Energy and Agriculture

IoT Machine Learning Applications in Telecom  Energy  and Agriculture Book
Author : Puneet Mathur
Publisher : Apress
Release : 2020-05-09
ISBN : 1484255496
Language : En, Es, Fr & De

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Book Description :

Apply machine learning using the Internet of Things (IoT) in the agriculture, telecom, and energy domains with case studies. This book begins by covering how to set up the software and hardware components including the various sensors to implement the case studies in Python. The case study section starts with an examination of call drop with IoT in the telecoms industry, followed by a case study on energy audit and predictive maintenance for an industrial machine, and finally covers techniques to predict cash crop failure in agribusiness. The last section covers pitfalls to avoid while implementing machine learning and IoT in these domains. After reading this book, you will know how IoT and machine learning are used in the example domains and have practical case studies to use and extend. You will be able to create enterprise-scale applications using Raspberry Pi 3 B+ and Arduino Mega 2560 with Python. What You Will Learn Implement machine learning with IoT and solve problems in the telecom, agriculture, and energy sectors with Python Set up and use industrial-grade IoT products, such as Modbus RS485 protocol devices, in practical scenarios Develop solutions for commercial-grade IoT or IIoT projects Implement case studies in machine learning with IoT from scratch Who This Book Is For Raspberry Pi and Arduino enthusiasts and data science and machine learning professionals.

Proceedings of the 4th Brazilian Technology Symposium BTSym 18

Proceedings of the 4th Brazilian Technology Symposium  BTSym 18  Book
Author : Yuzo Iano,Rangel Arthur,Osamu Saotome,Vânia Vieira Estrela,Hermes José Loschi
Publisher : Springer
Release : 2019-05-28
ISBN : 303016053X
Language : En, Es, Fr & De

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Book Description :

This book presents the Proceedings of The 4th Brazilian Technology Symposium (BTSym'18). Part I of the book discusses current technological issues on Systems Engineering, Mathematics and Physical Sciences, such as the Transmission Line, Protein-modified mortars, Electromagnetic Properties, Clock Domains, Chebyshev Polynomials, Satellite Control Systems, Hough Transform, Watershed Transform, Blood Smear Images, Toxoplasma Gondi, Operation System Developments, MIMO Systems, Geothermal-Photovoltaic Energy Systems, Mineral Flotation Application, CMOS Techniques, Frameworks Developments, Physiological Parameters Applications, Brain Computer Interface, Artificial Neural Networks, Computational Vision, Security Applications, FPGA Applications, IoT, Residential Automation, Data Acquisition, Industry 4.0, Cyber-Physical Systems, Digital Image Processing, Patters Recognition, Machine Learning, Photocatalytic Process, Physical-chemical analysis, Smoothing Filters, Frequency Synthesizers, Voltage Controlled Ring Oscillator, Difference Amplifier, Photocatalysis and Photodegradation. Part II of the book discusses current technological issues on Human, Smart and Sustainable Future of Cities, such as the Digital Transformation, Data Science, Hydrothermal Dispatch, Project Knowledge Transfer, Immunization Programs, Efficiency and Predictive Methods, PMBOK Applications, Logistics Process, IoT, Data Acquisition, Industry 4.0, Cyber-Physical Systems, Fingerspelling Recognition, Cognitive Ergonomics, Ecosystem services, Environmental, Ecosystem services valuation, Solid Waste and University Extension. BTSym is the brainchild of Prof. Dr. Yuzo Iano, who is responsible for the Laboratory of Visual Communications (LCV) at the Department of Communications (DECOM) of the Faculty of Electrical and Computing Engineering (FEEC), State University of Campinas (UNICAMP), Brazil.

Industry 4 0 AI and Data Science

Industry 4 0  AI  and Data Science Book
Author : Vikram Bali,Kakoli Banerjee,Narendra Kumar,Sanjay Gour,Sunil Kumar Chawla
Publisher : CRC Press
Release : 2021-07-21
ISBN : 1000413454
Language : En, Es, Fr & De

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Book Description :

The aim of this book is to provide insight into Data Science and Artificial Learning Techniques based on Industry 4.0, conveys how Machine Learning & Data Science are becoming an essential part of industrial and academic research. Varying from healthcare to social networking and everywhere hybrid models for Data Science, Al, and Machine Learning are being used. The book describes different theoretical and practical aspects and highlights how new systems are being developed. Along with focusing on the research trends, challenges and future of AI in Data Science, the book explores the potential for integration of advanced AI algorithms, addresses the challenges of Data Science for Industry 4.0, covers different security issues, includes qualitative and quantitative research, and offers case studies with working models. This book also provides an overview of AI and Data Science algorithms for readers who do not have a strong mathematical background. Undergraduates, postgraduates, academicians, researchers, and industry professionals will benefit from this book and use it as a guide.

Open Data and Energy Analytics

Open Data and Energy Analytics Book
Author : Benedetto Nastasi,Massimiliano Manfren,Michel Noussan
Publisher : MDPI
Release : 2020-06-25
ISBN : 3039362186
Language : En, Es, Fr & De

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Book Description :

Open data and policy implications coming from data-aware planning entail collection and pre- and postprocessing as operations of primary interest. Before these steps, making data available to people and their decision-makers is a crucial point. Referring to the relationship between data and energy, public administrations, governments, and research bodies are promoting the construction of reliable and robust datasets to pursue policies coherent with the Sustainable Development Goals, as well as to allow citizens to make informed choices. Energy engineers and planners must provide the simplest and most robust tools to collect, process, and analyze data in order to offer solid data-based evidence for future projections in building, district, and regional systems planning. This Special Issue aims at providing the state-of-the-art on open-energy data analytics; its availability in the different contexts, i.e., country peculiarities; and its availability at different scales, i.e., building, district, and regional for data-aware planning and policy-making. For all the aforementioned reasons, we encourage researchers to share their original works on the field of open data and energy analytics. Topics of primary interest include but are not limited to the following: 1. Open data and energy sustainability; 2. Open data science and energy planning; 3. Open science and open governance for sustainable development goals; 4. Key performance indicators of data-aware energy modelling, planning, and policy; 5. Energy, water, and sustainability database for building, district, and regional systems; 6. Best practices and case studies.

Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies

Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies Book
Author : Krishna Kumar,Ram Shringar Rao,Omprakash Kaiwartya,Shamim Kaiser,Sanjeevikumar Padmanaban
Publisher : Academic Press
Release : 2022-05-15
ISBN : 9780323912280
Language : En, Es, Fr & De

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Book Description :

Developments in the capacity for building renewable energy generation systems has sparked a paradigm shift in the energy structure and the sustainability sector. Sustainable Developments with Artificial Intelligence and Machine Learning for Renewable Energies analyses the changes in this energy generation shift, including issues of grid stability with the variability in renewable energy verses traditional baseload energy generation. The use of artificial intelligence to revolutionize the energy market and harness the potential of renewable energy is essential, this reference provides practical guidance on the application of renewable energy with AI, and machine learning and the capabilities to design, model, characterize and forecast performance predictions for the optimization of renewable energy systems. Providing solutions to current critical environmental, economic, and social issues, this book comprises various complex nonlinear interactions among different parameters to drive the integration of renewable energy into the grid. It considers how artificial intelligence and machine learning techniques are being developed to produce more reliable energy generation to optimize system performance and sustainable development. Targeted at researchers, academicians, and industry professionals working in the field of renewable energy, AI, machine learning, grid Stability and energy generation. Covers the best-performing methods and approaches for designing renewable energy systems where AI integration in a real-time environment with simulation results and online map hyperlinking Gives advanced techniques for monitoring current technologies, and how to efficiently utilize the energy grid spectrum Addresses the advance field of renewable generation, from research, impact, and idea development of new applications in a single platform

Handbook of Research on Smart Technology Models for Business and Industry

Handbook of Research on Smart Technology Models for Business and Industry Book
Author : Thomas, J. Joshua,Fiore, Ugo,Lechuga, Gilberto Perez,Kharchenko, Valeriy,Vasant, Pandian
Publisher : IGI Global
Release : 2020-06-19
ISBN : 1799836460
Language : En, Es, Fr & De

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Book Description :

Advances in machine learning techniques and ever-increasing computing power has helped create a new generation of hardware and software technologies with practical applications for nearly every industry. As the progress has, in turn, excited the interest of venture investors, technology firms, and a growing number of clients, implementing intelligent automation in both physical and information systems has become a must in business. Handbook of Research on Smart Technology Models for Business and Industry is an essential reference source that discusses relevant abstract frameworks and the latest experimental research findings in theory, mathematical models, software applications, and prototypes in the area of smart technologies. Featuring research on topics such as digital security, renewable energy, and intelligence management, this book is ideally designed for machine learning specialists, industrial experts, data scientists, researchers, academicians, students, and business professionals seeking coverage on current smart technology models.

Application of Machine Learning and Deep Learning Methods to Power System Problems

Application of Machine Learning and Deep Learning Methods to Power System Problems Book
Author : Morteza Nazari-Heris,Somayeh Asadi,Behnam Mohammadi-Ivatloo,Moloud Abdar,Houtan Jebelli,Milad Sadat-Mohammadi
Publisher : Springer Nature
Release : 2021-11-21
ISBN : 3030776964
Language : En, Es, Fr & De

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Book Description :

This book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction, classification, clustering, and fault/event detection in power systems, providing effective and promising solutions for many novel challenges faced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses.

Profit Maximization Techniques for Operating Chemical Plants

Profit Maximization Techniques for Operating Chemical Plants Book
Author : Sandip K. Lahiri
Publisher : John Wiley & Sons
Release : 2020-05-01
ISBN : 1119532175
Language : En, Es, Fr & De

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Book Description :

A systematic approach to profit optimization utilizing strategic solutions and methodologies for the chemical process industry In the ongoing battle to reduce the cost of production and increase profit margin within the chemical process industry, leaders are searching for new ways to deploy profit optimization strategies. Profit Maximization Techniques For Operating Chemical Plants defines strategic planning and implementation techniques for managers, senior executives, and technical service consultants to help increase profit margins. The book provides in-depth insight and practical tools to help readers find new and unique opportunities to implement profit optimization strategies. From identifying where the large profit improvement projects are to increasing plant capacity and pushing plant operations towards multiple constraints while maintaining continuous improvements—there is a plethora of information to help keep plant operations on budget. The book also includes information on: ● Take away methods and techniques for identifying and exploiting potential areas to improve profit within the plant ● Focus on latest Artificial Intelligence based modeling, knowledge discovery and optimization strategies to maximize profit in running plant. ● Describes procedure to develop advance process monitoring and fault diagnosis in running plant ● Thoughts on engineering design , best practices and monitoring to sustain profit improvements ● Step-by-step guides to identifying, building, and deploying improvement applications For leaders and technologists in the industry who want to maximize profit margins, this text provides basic concepts, guidelines, and step-by-step guides specifically for the chemical plant sector.

Machine Learning for Sustainable Development

Machine Learning for Sustainable Development Book
Author : Kamal Kant Hiran,Deepak Khazanchi,Ajay Kumar Vyas,Sanjeevikumar Padmanaban
Publisher : Walter de Gruyter GmbH & Co KG
Release : 2021-07-19
ISBN : 3110702517
Language : En, Es, Fr & De

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Book Description :

The book will focus on the applications of machine learning for sustainable development. Machine learning (ML) is an emerging technique whose diffusion and adoption in various sectors (such as energy, agriculture, internet of things, infrastructure) will be of enormous benefit. The state of the art of machine learning models is most useful for forecasting and prediction of various sectors for sustainable development.

Data Science and Analytics

Data Science and Analytics Book
Author : Usha Batra,Nihar Ranjan Roy,Brajendra Panda
Publisher : Springer Nature
Release : 2020-05-27
ISBN : 9811558272
Language : En, Es, Fr & De

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Book Description :

This two-volume set (CCIS 1229 and CCIS 1230) constitutes the refereed proceedings of the 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, held in Gurugram, India, in November 2019. The 74 revised full papers presented were carefully reviewed and selected from total 353 submissions. The papers are organized in topical sections on data centric programming; next generation computing; social and web analytics; security in data science analytics; big data analytics.

Data Science for Wind Energy

Data Science for Wind Energy Book
Author : Yu Ding
Publisher : CRC Press
Release : 2019-06-04
ISBN : 0429956509
Language : En, Es, Fr & De

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Book Description :

Data Science for Wind Energy provides an in-depth discussion on how data science methods can improve decision making for wind energy applications, near-ground wind field analysis and forecast, turbine power curve fitting and performance analysis, turbine reliability assessment, and maintenance optimization for wind turbines and wind farms. A broad set of data science methods covered, including time series models, spatio-temporal analysis, kernel regression, decision trees, kNN, splines, Bayesian inference, and importance sampling. More importantly, the data science methods are described in the context of wind energy applications, with specific wind energy examples and case studies. Features Provides an integral treatment of data science methods and wind energy applications Includes specific demonstration of particular data science methods and their use in the context of addressing wind energy needs Presents real data, case studies and computer codes from wind energy research and industrial practice Covers material based on the author's ten plus years of academic research and insights

Big Data Application in Power Systems

Big Data Application in Power Systems Book
Author : Reza Arghandeh,Yuxun Zhou
Publisher : Elsevier Science
Release : 2017-09-01
ISBN : 9780128119686
Language : En, Es, Fr & De

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Book Description :

Big Data Application in Power Systems brings together experts from academia, industry and regulatory agencies who share their understanding and discuss the big data analytics applications for power systems diagnostics, operation and control. Recent developments in monitoring systems and sensor networks dramatically increase the variety, volume and velocity of measurement data in electricity transmission and distribution level. The book focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data. The book chapters discuss challenges, opportunities, success stories and pathways for utilizing big data value in smart grids. Provides expert analysis of the latest developments by global authorities Contains detailed references for further reading and extended research Provides additional cross-disciplinary lessons learned from broad disciplines such as statistics, computer science and bioinformatics Focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data

Smart Meter Data Analytics

Smart Meter Data Analytics Book
Author : Yi Wang,Qixin Chen,Chongqing Kang
Publisher : Springer Nature
Release : 2020-02-24
ISBN : 9811526249
Language : En, Es, Fr & De

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Book Description :

This book aims to make the best use of fine-grained smart meter data to process and translate them into actual information and incorporated into consumer behavior modeling and distribution system operations. It begins with an overview of recent developments in smart meter data analytics. Since data management is the basis of further smart meter data analytics and its applications, three issues on data management, i.e., data compression, anomaly detection, and data generation, are subsequently studied. The following works try to model complex consumer behavior. Specific works include load profiling, pattern recognition, personalized price design, socio-demographic information identification, and household behavior coding. On this basis, the book extends consumer behavior in spatial and temporal scale. Works such as consumer aggregation, individual load forecasting, and aggregated load forecasting are introduced. We hope this book can inspire readers to define new problems, apply novel methods, and obtain interesting results with massive smart meter data or even other monitoring data in the power systems.

Deep Learning in Data Analytics

Deep Learning in Data Analytics Book
Author : Debi Prasanna Acharjya
Publisher : Springer Nature
Release : 2021
ISBN : 3030758559
Language : En, Es, Fr & De

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Book Description :

This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society. Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.